Introduction
Welcome to the Upper Missouri River Basin (UMRB) drought indicators dashboard.
This website provides access to several drought metrics commonly used across the globe. All datasets are calculated daily and can be aggregated by watershed and county boundaries. In this document we focus on:
- Providing current drought information using the most current peer reviewed science
- Describing general overview and theoretical basis for a metric
- Describe the data required for calculation
- Discuss the relative strengths and weaknesses of a metric
- Validating a metric using on the ground observations (soil moisture, streamflow and groundwater; ongoing)
This work is supported by a National Oceanic and Atmospheric Administration’s (NOAA) National Integrated Drought Information System (NIDIS) Drought Early Warning System (DEWS) grant. More information on NIDIS and the DEWS program can be found at https://www.drought.gov/drought/what-nidis.
Select a drought indicator from the above tabs to analyze current conditions.
Snow Water Equivalent
Snow Telemetry (SNOTEL)
Overview:
SNOTEL is an automated system of snowpack and related enviormental sensors. SNOTEL is operated by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture in the Western United States. Plots in this module show total snow water equievelant (SWE) and total liquid precipitation (rain + SWE) measured to date compared to historical averages.
Row
Snow Water Equivalent
Accumulated Precipitation
Snow Telemetry (SNOTEL)
Overview:
SNOTEL is an automated system of snowpack and related enviormental sensors. SNOTEL is operated by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture in the Western United States. Plots in this module show total snow water equievelant (SWE) and total liquid precipitation (rain + SWE) measured to date compared to historical averages.
Row
Accumulated Precipitation
Precipitation Percentiles
Precipitation Percentiles
Overview:
Precipitation is a strong driver of drought. In most cases, drought begins with a precipitation deficit which is the exacerbated by atmospheric conditions (such as temperature, humidity and wind speed). In this module we present precipitation percentiles calculated over different timescales. 50 represents the average (median) precipitation over the time period. 100 and 0 represent the wettest and driest conditions observed respectively.
Data Requirements:
- Precipitation
Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.
Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.
Row
Gridded Data
Counties
Watersheds
Row
Derivation:
For each timescale we calculate the accumulated precipitation for each year across the period of record (1979 - present) and compute the current percentile value.
Key Strengths:
- Easily calculated, uses only precipitation data.
- Easily interpreted
Key Weaknesses:
- Doesn’t account for atmospheric demand of moisture, this limits identification of flash drought due to high temperature and large vapor pressure deficits
- Sensitive to biases in precipitation records over time
- Does not account for the capacity of the landscape to capture precipitation or generate runoff
Precipitation Anomaly (% of Normal)
Precipitation Anomaly
Overview:
Precipitation is a strong driver of drought. In most cases, drought begins with a precipitation deficit which is then exacerbated by atmospheric conditions (such as temperature, humidity and wind speed). In this module we present precipitation anomalies calculated over different timescales. For each timescale we calculate the average accumulated precipitation across the period of record (1979 - present) and compute the percent of average. 100% represents average expected precipitation.
Data Requirements:
- Precipitation
Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.
Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.
Row
Gridded Data
Counties
Watersheds
Row
Derivation:
For each timescale we calculate the average accumulated precipitation across the period of record (1979 - present) and compute the percent of average. 100% represents average expected precipitation.
Key Strengths:
- Easily calculated, uses only precipitation data.
- Easily interpreted
Key Weaknesses:
- Doesn’t account for atmospheric demand of moisture, this limits identification of flash drought due to high temperature and large vapor pressure deficits
- Sensitive to biases in precipitation records over time
- Does not account for the capacity of the landscape to capture precipitation or generate runoff
Standardized Precipitation Index (SPI)
Standardized Precipitation Index (SPI)
Overview:
The SPI is a commonly used metric which quantifies precipitation anomalies at various timescales. This metric is often used to estimate a range of hydrological conditions that respond to precipation over differing timescales. For example, SPI is related to soil moisture anomalies when calculated over short time scales (days to weeks) but is more related to groundwater and reservoir storage over longer timescales (months to years). The values of SPI can be interpreted as a number of standard deviations away from the average (mean) cumulative precipitation depth for a given time period. The SPI has unique statistical qualities in that it is directly related to precipitation probaility and it can be used to represent both dry (negative values; represented here with warmer colors) and wet (positive values; represented here with cooler colors) conditions.
Data Requirements:
- Precipitation
Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.
Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.
Row
Gridded Data
Counties
Watersheds
Row
Derivation:
The SPI quantifies precipitation as a standardized departure from a selected probability distribution function that models the raw precipitation data. The raw precipitation data are typically fitted to a gamma or a Pearson Type III distribution, and then transformed to a normal distribution (Keyantash and NCAR staff, 2018). Normalization of data is important because precipitation data is heavily right hand skewed. This is because smaller precipitation events are much more probable than large events.
Key Strengths:
- Easily calculated, uses only precipitation data
- Can be used to estimate effects of drought or water abundance on differing hydrologic reservoirs (e.g. soil moisture, groundwater, etc) using different time scales
- Comparable in across regions (within reason) due to normalization of data
- Can account for changes in climatology as probability distributions are updated through time
Key Weaknesses:
- Doesn’t account for atmospheric demand of moisture, this limits identification of flash drought due to high temperature and large vapor pressure deficits
- Sensitive to biases in precipitation records over time
- Does not account for the capacity of the landscape to capture precipitation or generate runoff
Historical Validation:
There has been extensive validation of the SPI across the globe. In general, results have shown that the SPI provides similar results to different standardized precipitation indices.
https://www.sciencedirect.com/science/article/pii/S0168192318303708
https://link.springer.com/article/10.1007/s10584-005-5358-9
https://www.hydrol-earth-syst-sci.net/17/2359/2013/hess-17-2359-2013.html
https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-13-0190.1
https://journals.ametsoc.org/doi/abs/10.1175/JAMC-D-10-05015.1
UMRB Validation:
Validation in progress
UMRB Recommendation:
UMRB specific recommendations will be appended to this document as validation is completed
Much of the background information regarding this metric was contributed by NCAR/UCAR Climate Data Guide
Standardized Precipitation Evapotranspiration Index (SPEI)
Standardized Precipitation Evapotranspiration Index (SPEI)
Overview:
SPEI takes into account both precipitation and potential evapotranspiration to describe the wetness/dryness of a time period. Similar to the SPI, SPEI can be calculated at various timescales to represent different drought timescales. As such, the SPEI can approximate different impacts of drought on hydrological conditions and processes depending on the timescale used. Although similar to the SPI, SPEI incorporates the import effect of atmospheric demand on drought which can cause significant impacts over short time scales (flash drought).
Data Requirements:
- Precipitation
- Potential Evapotranspiration (PET)
Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.
Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.
Row
Gridded Data
Counties
Watersheds
Row
Derivation:
SPEI is an extension of the SPI in the sense that it uses a normalized probability distribution approximation of raw values to calculate deviation from normals. Similar to SPI, SPEI values are reported in units of standard deviation or z-score (Vicente-Serrano and NCAR staff, 2015). Although, the raw values for this metric are P-PET.
Key Strengths:
- Combines information about water availability (precipitation) and atmospheric demand for moisture (potential evapotranspiration)
- Relatively simple to calculate and only requires climatological data and statistical models
- Does not incorporate assumptions about the behavior of the underlying system
- Can be calculated where only T and P exist (if using Thornthwaite based PET)
- Can be calculated with more sophisticated PET algorithms if data is available
Key Weaknesses:
- Sensitive to differences in PET calculations
- Requires climatology of data to have accurate statistical reference distributions
- Sensitive to probability distribution used to normalize data distribution
Historical Validation:
The SPEI has been used in many studies to understand the effects of drought on hydrologic resource availability, including reservoir, stream discharge and groundwater. In general, SPEI calculated at longer timescales (>12 months) has shown greater correlation with water levels in lakes and reservoirs (McEvoy et al., 2012).
UMRB Validation:
Validation in progress
UMRB Recommendation:
UMRB specific recommendations will be appended to this document as validation is completed
Much of the background information regarding this metric was adapted from the NCAR/UCAR Climate Data Guide (here)
Evaporative Demand Drought Index (EDDI)
Evaporative Demand Drought Index (EDDI)
Overview:
EDDI calculates the rank of accumulated PET for a given region. Unlike SPI and SPEI, EDDI does not standardize data based off of theoretical (parameterized) probability distributions. Instead, EDDI uses a non-parametric approach to compute empirical probabilities using inverse normal approximation. This method calculates EDDI by ranking the data from smallest to largest and accounting for the number of observations. Therefore, maximum and minimum values of EDDI are constrained by the number of years on record (which determines the number of observations). Practically, this causes EDDI to only show the relative ranking of year, with respect to the period of record, and does not approximate the magnitude of PET anomalies outside of relative ranking.
Data Requirements:
- Potential Evapotranspiration (PET)
Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.
Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.
Row
Gridded Data
Counties
Watersheds
Row
Derivation:
Key Strengths:
Key Weaknesses:
Historical Validation:
UMRB Validation:
Validation in progress
UMRB Recommendation:
UMRB specific recommendations will be appended to this document as validation is completed
Much of the background information regarding this metric was adapted from the NCAR/UCAR Climate Data Guide (here)
Standardized Evapotranspiration Deficit Index (SEDI)
Standardized Evapotranspiration Deficit Index (SEDI)
Overview:
SEDI calculates the standardized difference between actual evapotranspiration (AET or ETa) and potential evapotranspiration (PET) for a given region. Similar to the SPI, SEDI can be calculated at various timescales to represent different drought timescales. As such, the SEDI can approximate different impacts of drought on hydrological conditions and processes depending on the timescale used. SEDI is a relatively new drought metric that has advantages over strictly meteorologically based drought metrics (such as SPI) because it uses a soil water balance model to estimate water available for evapotranspiration (AET or ETa). Therefore SEDI represents the standardized unmet atmospheric demand for moisture, which is a good representation of potential vegetation water stress.
Data Requirements:
- Actual Evapotranspiration (AET or ETa)
- Potential Evapotranspiration (PET)
Select a timescale from the left panel on the map. You can also toggle overlays, such as the current US Drought Monitor (USDM) drought designations, weather (from the National Weather Service) and state boundaries.
Tabs above the map allow you to aggregate the data by political or hydrologic boundaries.
Row
Gridded Data
Counties
Watersheds
Row
Derivation:
Key Strengths:
Key Weaknesses:
Historical Validation:
UMRB Validation:
Validation in progress
UMRB Recommendation:
UMRB specific recommendations will be appended to this document as validation is completed
Much of the background information regarding this metric was adapted from the NCAR/UCAR Climate Data Guide (here)